โก Quick Summary
This study introduces an AI-based algorithm designed to analyze physical activity and health-related fitness in youth, utilizing machine learning techniques to enhance the accuracy of physical fitness assessments. The algorithm achieved an impressive 98.448% classification performance and offers personalized training suggestions for students.
๐ Key Details
- ๐ Dataset: Physical fitness test data from primary schools (2018-2022)
- ๐งฉ Techniques used: BP neural network for classification, CNN-LSTM for performance prediction
- ๐ Performance: BP neural network: 98.448% classification accuracy
- ๐ Applications: Personalized training suggestions and improved teaching plans
๐ Key Takeaways
- ๐ค AI algorithms can significantly enhance the evaluation of physical fitness in youth.
- ๐ The BP neural network achieved a remarkable classification performance of 98.448%.
- ๐ The CNN-LSTM model provides accurate predictions for various physical test items.
- ๐ก Personalized training suggestions can assist teachers in developing effective teaching plans.
- ๐ฑ This approach addresses traditional evaluation challenges such as subjectivity and data retention.
- ๐ซ The study promotes the healthy development of students through data-driven insights.
- ๐ The research spans five years, highlighting trends in youth fitness.
๐ Background
The health status of primary and secondary school students has garnered significant attention, particularly in light of national fitness initiatives. Traditional methods of assessing physical fitness often suffer from subjective biases and complicated manual calculations, making it challenging to retain and utilize data effectively. This study aims to leverage advanced technologies to overcome these limitations and enhance the management of physical fitness evaluations.
๐๏ธ Study
Conducted over five years, this study analyzed physical fitness test data from primary schools to develop an AI-based algorithm that employs machine learning and deep learning techniques. The research focused on creating automatic classification methods and accurate performance prediction models, ultimately providing personalized training suggestions for students.
๐ Results
The study’s findings revealed that the BP neural network achieved an outstanding classification performance of 98.448%, effectively categorizing students’ physical health and fitness levels. Additionally, the CNN-LSTM model demonstrated its capability to predict performance across various physical test items, offering a new avenue for managing and evaluating fitness results in youth.
๐ Impact and Implications
The implications of this study are profound, as it not only addresses the shortcomings of traditional evaluation methods but also provides a framework for schools to enhance their physical education programs. By integrating AI technologies, educators can offer personalized training suggestions that cater to individual student needs, promoting healthier lifestyles and improved fitness outcomes among youth.
๐ฎ Conclusion
This research highlights the transformative potential of AI in physical fitness assessment for youth. By utilizing machine learning algorithms, schools can achieve more accurate evaluations and foster the healthy development of students. The future of physical education looks promising with the integration of these innovative technologies, paving the way for enhanced student engagement and fitness.
๐ฌ Your comments
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An AI-based algorithm for analyzing physical activity and health-related fitness in youth.
Abstract
In recent years, with the country’s emphasis on national fitness, the health status of primary and secondary school students has become the focus of social attention. As one of the important means to measure students’ physical fitness, physical examination results are closely related to students’ physical fitness. However, there are some problems in the traditional physical examination management, such as subjective influence, complicated manual calculation, and difficulty in retaining and making full use of data. Based on the physical fitness test data of primary schools in the past five years from 2018 to 2022, this study aims to apply machine learning and deep learning methods to deeply analyze and mine data information, provide automatic classification methods and accurate performance prediction models, and then expand to provide students with personalized training suggestions to assist teachers in making reasonable teaching plans and other applications. The first research method is the classification method based on BP neural network, which realizes automatic comprehensive grade classification and achieves 98.448% classification performance, and explores students’ physical health and grade classification. The second research method is the performance prediction model based on CNN-LSTM neural network, which combines CNN feature matrix and LSTM continuous time series information to provide more accurate performance prediction for various physical test items, and provides a new method for the management and evaluation of physical test results of primary and secondary school students through data analysis and prediction model. These methods not only solve the problems of traditional evaluation methods, but also provide scientific guidance for schools and promote the healthy development of students and the optimization of physical education.
Author: [‘Lv M’, ‘Wang J’, ‘Yang Y’, ‘Zhong J’, ‘Yang J’, ‘Dong C’]
Journal: Sci Rep
Citation: Lv M, et al. An AI-based algorithm for analyzing physical activity and health-related fitness in youth. An AI-based algorithm for analyzing physical activity and health-related fitness in youth. 2026; (unknown volume):(unknown pages). doi: 10.1038/s41598-026-35514-5